vi_survey_analysis

Analysis of the survey result

Setup

Code

library(visage)
library(tidyverse)

Code

vi_lineup <- readRDS(here::here("data/vi_lineup.rds"))
poly_conv_sim <- readRDS(here::here("data/poly_conventional_simulation.rds"))
heter_conv_sim <- readRDS(here::here("data/heter_conventional_simulation.rds"))

Overview

The survey dataset vi_survey contains 38 columns.

Code

knitr::kable(data.frame(names(vi_survey), 
                        c("Experiment ID",
                          "Unique lineup ID",
                          "Lineup ID in an experiment",
                          "Page number of the study website",
                          "Subject ID in an experiment",
                          "The count of the lineup presented to a subject",
                          "Response time to a page",
                          "Selected plots in a lineup",
                          "Number of selected plots",
                          "The position of the actual data plot",
                          "Whether the subject selected the actual data plot",
                          "Weighted detect is `detect`/`num_selection` and 1/20 for `num_selection == 0`",
                          "Average weighted detect for a lineup",
                          "Effect size of a lineup",
                          "Coventional p-value",
                          "Visual p-value",
                          "Reason for the selection",
                          "How different the selected plot from others",
                          "Age group of the subject",
                          "Education background of the subject",
                          "Pronoun",
                          "Whether the subject has previous experience in experiment about reading data plots",
                          "Type of the lineup",
                          "Formula of the data genearting processing",
                          "Parameter `shape` of the polynomial model",
                          "Parameter `a` of the heteroskedasticity model",
                          "Parameter `b` of the heteroskedasticity model",
                          "Distribution of the regressor",
                          "Distribution of the error term",
                          "Standard deviation of the polynomial model",
                          "Whether to include polynomial terms",
                          "Name of the model",
                          "Number of plots in a lineup",
                          "Number of observations in a plot",
                          "c-interesting plots in a lineup",
                          "Estimated alpha of the Dirichelt model",
                          "Sum of squre error of the alpha estimate",
                          "Expected effect size")), col.names = c("Variable", "Description")) %>%
  kableExtra::kable_material_dark(bootstrap_options = c("striped", "hover"))

Variable	Description
exp	Experiment ID
unique_lineup_id	Unique lineup ID
lineup_id	Lineup ID in an experiment
page	Page number of the study website
set	Subject ID in an experiment
num	The count of the lineup presented to a subject
response_time	Response time to a page
selection	Selected plots in a lineup
num_selection	Number of selected plots
answer	The position of the actual data plot
detect	Whether the subject selected the actual data plot
weighted_detect	Weighted detect is `detect`/`num_selection` and 1/20 for `num_selection == 0`
prop_detect	Average weighted detect for a lineup
effect_size	Effect size of a lineup
conventional_p_value	Coventional p-value
p_value	Visual p-value
reason	Reason for the selection
confidence	How different the selected plot from others
age_group	Age group of the subject
education	Education background of the subject
pronoun	Pronoun
previous_experience	Whether the subject has previous experience in experiment about reading data plots
type	Type of the lineup
formula	Formula of the data genearting processing
shape	Parameter `shape` of the polynomial model
a	Parameter `a` of the heteroskedasticity model
b	Parameter `b` of the heteroskedasticity model
x_dist	Distribution of the regressor
e_dist	Distribution of the error term
e_sigma	Standard deviation of the polynomial model
include_z	Whether to include polynomial terms
name	Name of the model
k	Number of plots in a lineup
n	Number of observations in a plot
c_interesting	c-interesting plots in a lineup
alpha	Estimated alpha of the Dirichelt model
alpha_sum_sq_error	Sum of squre error of the alpha estimate
expected_effect_size	Expected effect size

Code

null_lineups <- filter(vi_survey, b == 0)
vi_survey <- filter(vi_survey, lineup_id <= 576) %>%
  mutate(subject = as.numeric(factor(interaction(exp, set))))

The survey data has 7974 lineup evaluations for 1152 unique lineups evaluated by 443 subjects. Experiment I has 2880 lineup evaluations for 576 unique lineups evaluated by 160 subjects. Experiment II has 2880 lineup evaluations for 576 unique lineups evaluated by 160 subjects. Experiment III has 2214 lineup evaluations for 315 unique lineups evaluated by 123 subjects. There are 720 lineup evaluations for 36 Rorschach lineups.

Demographic

The education background of most participants have Diploma or Bachelor degrees, followed by High school or below. The survey data is gender balanced. Most of the participants are between 18 to 39.

Code

vi_survey %>%
  mutate(pronoun = ifelse(pronoun == "They", "Other", pronoun)) %>%
  ggplot() +
  geom_bar(aes(age_group, fill = pronoun), position = "dodge") +
  facet_wrap(~fct_relevel(education, "High School or below", "Diploma and Bachelor Degree", "Honours Degree", "Masters Degree")) +
  xlab("Age group") +
  ylab("Count")

Number of participants who have previous experience in experiment about reading data plots are not very different from the number of participants who haven’t. Age distributions are also similar for these two groups.

Code

vi_survey %>%
  mutate(pronoun = ifelse(pronoun == "They", "Other", pronoun)) %>%
  ggplot() +
  geom_bar(aes(age_group, fill = pronoun), position = "dodge") +
  facet_wrap(~previous_experience) +
  xlab("Age group") +
  ylab("Count")

Response time

The distribution of the log response time is slightly right skewed.

Code

vi_survey %>%
  ggplot() +
  geom_density(aes(response_time/1000)) +
  scale_x_log10() +
  xlab("Response time (seconds)") +
  ggtitle("Distribution of response time in log scale")

Subjects spend less time as the experiment progress.

Code

vi_survey %>%
  ggplot() +
  geom_boxplot(aes(num, response_time/1000, group = num)) +
  xlab("#lineup") +
  ylab("Response time (seconds)") +
  scale_y_log10() +
  ggtitle("Boxplots of response time in log scale vs the order of lienup")

The minimum response time is 3.429 seconds given by subject 188 while evaluating lineup heter_292. This subject get it wrong with 0 selections. This is the #12 lineup presented to the subject. The total amount of time this subject spent on the study is 568.44 seconds.

The maximum response time is 1885.9174 seconds given by subject 11 while evaluating lineup heter_497. This subject get it wrong with 0 selections. This is the #1 lineup presented to the subject. The total amount of time this subject spent on the study is 2549.26 seconds.

The median response time is 29.89015 seconds.

The median total response time per subject is 682.8373 seconds. Four subjects spent more time than others in the study. Subject 11 spent a long time on the first lineup. Subject 163, 176 and 246 consistently evaluates lineups in a slow pace.

Code

vi_survey %>%
  group_by(subject) %>%
  summarise(total_response_time = sum(response_time)/1000) %>%
  ggplot() +
  geom_boxplot(aes(total_response_time)) +
  ggrepel::geom_label_repel(data = ~tail(arrange(., total_response_time), 4), aes(total_response_time, 0, label = subject)) +
  scale_x_log10() +
  xlab("Total response time per subject") +
  ggtitle("Total response time per subject in log scale")

Code

vi_survey %>%
  filter(subject %in% c(163, 176, 11, 246)) %>%
  ggplot() +
  geom_line(aes(num, response_time/1000, col = factor(subject))) +
  labs(col = "subject") +
  scale_y_log10() +
  xlab("#lineup") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs the order of lienup")

Code

vi_survey %>%
  filter(subject %in% c(163, 176, 11, 246)) %>%
  select(subject, num, response_time) %>%
  mutate(response_time = response_time/1000) %>%
  pivot_wider(names_from = num, values_from = response_time) %>%
  select(subject, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`, `9`, `10`, `11`, `12`, `13`, `14`, `15`, `16`, `17`, `18`, `19`, `20`) %>%
  arrange(subject) %>%
  knitr::kable("markdown")

subject	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20
11	1885.9174	NA	49.2996	22.3551	124.5578	26.3431	105.3199	37.7153	14.9377	53.7889	NA	8.7748	38.9998	17.0089	13.1477	94.8472	14.8883	9.6115	19.7397	12.0036
163	280.0926	278.2569	419.8284	311.6960	228.4427	170.6173	299.3044	390.6301	176.7911	251.1547	146.0632	NA	87.8268	84.2275	133.4889	115.4194	206.3442	435.9604	NA	496.7355
176	307.5907	140.9978	159.0287	173.3488	402.5440	49.1973	94.7572	494.5561	447.2343	202.1525	130.6681	250.6120	113.2287	45.9538	33.8734	NA	NA	248.3166	131.2025	93.1567
246	235.0557	679.9166	NA	139.7279	193.9005	85.5134	NA	67.2221	86.5729	129.4547	64.1542	66.0654	58.4318	105.6275	109.9599	151.8416	29.9520	47.5295	56.0373	64.7908

When the subject detect the data plot, the response time is usually lower. This is because it is easier to make decision on lineup with obvious patterns.

Code

vi_survey %>%
  ggplot() +
  geom_density(aes(response_time/1000, col = detect)) +
  scale_x_log10() +
  facet_wrap(~type) +
  xlab("Response time (seconds)") +
  ylab("Density") +
  ggtitle("Distribution of response time in log scale")

There is a very weak negative relationship between the difficulty of the lineup and the response time. There are some cases where the lineup is very difficult, but the subject evaluates the lineup in a short time. But there are no obvious outliers except for one we have analysed.

Code

vi_survey %>%
  ggplot() +
  geom_point(aes(effect_size, response_time/1000), alpha = 0.1) +
  geom_smooth(aes(effect_size, response_time/1000)) +
  scale_y_log10() +
  scale_x_log10() +
  facet_grid(detect~type) +
  xlab("Effect size") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs effect size in log scale")

For Rorschach lineup, the response time varies a lot, from few seconds to several minutes.

Code

vi_survey %>%
  filter(b == 0) %>%
  ggplot() +
  geom_density(aes(response_time/1000)) +
  scale_x_log10() +
  xlab("Response time (seconds)") +
  ylab("Count") +
  ggtitle("Response time for Rorschach lineup in log scale") +
  facet_wrap(~detect)

Number of selection made by the subjects seems to be correlated to the response time. Subjects tend to spend more time on a lineup if they select more plots, which is reasonable.

Code

vi_survey %>%
  ggplot() +
  geom_boxplot(aes(num_selection, response_time/1000, group = factor(num_selection, levels = 0:20))) +
  geom_smooth(aes(num_selection, response_time/1000)) +
  scale_y_log10() +
  scale_x_continuous(breaks = 0:20, minor_breaks = NULL) +
  facet_grid(type~detect) +
  xlab("Number of selections") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs number of selection faceted by detection and type of model")

When subjects correctly identify the actual data plot, they tend to spend less time if they discover a shape from the plot, followed by cluster(s) and outlier(s). Subjects spend much more time for reason “Others”, which they need to manually provide a custom reason. This could be due to the fact that they need to spend time in typing the reason. However, we do not see the same difference when they do not detect the actual data plot. The median response time are similar for different reasons.

Code

vi_survey %>%
  mutate(reason = ifelse(reason %in% c("Shape", "Cluster(s)", "Outlier(s)"), reason, "Others")) %>%
  mutate(reason = factor(reason, levels = c("Shape", "Cluster(s)", "Outlier(s)", "Others"))) %>%
  ggplot() +
  geom_boxplot(aes(reason, response_time/1000)) +
  scale_y_log10() +
  facet_grid(type~detect) +
  xlab("Reason to select the plot") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs reason faceted by detection and type of model")

When subjects correctly identify the actual data plot, the response time decreases as the confidence level increases. When subjects do not detect the actual data plot, they tend to spend less time if they do not find anything particularly interesting for the heteroskedasticity model. Curiously, they spend more time even though they think the selected plot is extremely different to others for the polynomial model. This could be due to the small sample size (seven). And three out of seven evaluations are given by the same subject 202.

Code

vi_survey %>%
  ggplot() +
  geom_boxplot(aes(factor(confidence, levels = c("Not at all", "Slightly", "Moderately", "Very", "Extremely")), response_time/1000)) +
  scale_y_log10() +
  facet_grid(type~detect) +
  xlab("Confidence") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs confidence faceted by detection and type of model")

We do not observe any particular structure for the distribution of the log of the response time when the answer is placed in different panels.

Code

vi_survey %>%
  ggplot() +
  geom_boxplot(aes(answer, response_time/1000, group = answer)) +
  scale_y_log10() +
  facet_grid(type~detect) +
  xlab("Answer") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs answer faceted by detection and type of model")

It is interesting to see that the response time is greater for older subjects in regards of the type of the model and whether the answer is correct.

Code

vi_survey %>%
  ggplot() +
  geom_boxplot(aes(age_group, response_time/1000)) +
  scale_y_log10() +
  facet_grid(type~detect) +
  xlab("Age group") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs age group faceted by detection and type of model")

Female subjects tend to spend little more time than male in our experiments. The “40-54” and “55-64” age groups show larger difference between male and female.

Code

vi_survey %>%
  ggplot() +
  geom_boxplot(aes(age_group, response_time/1000, col = ifelse(pronoun == "They", "Other", pronoun))) +
  scale_y_log10() +
  labs(col = "pronoun") +
  xlab("Age group") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs age group colored by pronoun")

The same upward trend of response time maintains for different education groups.

Code

vi_survey %>%
  ggplot() +
  geom_boxplot(aes(age_group, response_time/1000)) +
  scale_y_log10() +
  facet_wrap(~fct_relevel(education, "High School or below", "Diploma and Bachelor Degree", "Honours Degree", "Masters Degree")) +
  labs(col = "pronoun") +
  xlab("Age group") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs age group faceted by pronoun")

However, notice that some groups have only few observations. Thus, the trend might not be reliable.

Code

vi_survey %>%
  group_by(age_group, education) %>%
  summarise(num_subject = length(unique(subject))) %>%
  ggplot() +
  geom_bar(aes(age_group, num_subject), stat = "identity") +
  facet_wrap(~fct_relevel(education, "High School or below", "Diploma and Bachelor Degree", "Honours Degree", "Masters Degree"))

The median response time of the group “Doctoral Degree:FALSE:polynomial” is higher than others. It only contains evaluations given by two subjects. Apart from that, we do not observe any trend from the plot.

Code

vi_survey %>%
  ggplot() +
  geom_boxplot(aes(fct_relevel(education, "High School or below", "Diploma and Bachelor Degree", "Honours Degree", "Masters Degree"), response_time/1000)) +
  scale_y_log10() +
  facet_grid(type~detect) + 
  xlab("Education background") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs education background faceted by detection and type of model")

Subjects with previous experience doesn’t seem like to evaluate lineups faster.

Code

vi_survey %>%
  ggplot() +
  geom_boxplot(aes(previous_experience, response_time/1000)) +
  scale_y_log10() +
  facet_grid(type~detect) + 
  xlab("Previous experience") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs previous experience faceted by detection and type of model")

For polynomial model, different shapes do not affect the response time much.

Code

vi_survey %>%
  filter(type == "polynomial") %>%
  ggplot() +
  geom_boxplot(aes(factor(shape), response_time/1000)) +
  scale_y_log10() +
  facet_grid(~detect) +
  xlab("Shape") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs shape for polynomial model")

Different distributions of \(x\) slightly affect the response time. The response time for uniform distribution is lower when the subject correctly identify the actual data plot. Additionally, the response time for lognormal distribution is higher than others. This could be due to uniform distribution has a higher chance to reveal the underlying visual pattern, and lognormal distribution is the opposite.

Code

vi_survey %>%
  filter(type == "polynomial") %>%
  ggplot() +
  geom_boxplot(aes(factor(x_dist, levels = c("uniform", "even_discrete", "normal", "lognormal")), response_time/1000)) +
  scale_y_log10() +
  facet_grid(~detect) +
  xlab("Distribution of x") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs distribution of x for polynomial model faceted by detection")

It is interesting to see that the response times are not much different across different \(\sigma\) values when the subject can not detect the data plot, except for \(n = 300\). For \(n = 300\), the response time is lower for lower noise level, indicating that when the visual pattern is visible and detectable, subjects will spend less time in making the decision. When the subject correctly identify the actual data plot, the same trend occurs in clearer manner.

Code

vi_survey %>%
  filter(type == "polynomial") %>%
  ggplot() +
  geom_boxplot(aes(factor(e_sigma), response_time/1000)) +
  scale_y_log10() +
  facet_grid(n~detect) +
  xlab("Noise level (sigma)") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs noise level for polynomial model faceted by detection")

We do not observe any interesting pattern from the response time vs alpha plot.

Code

vi_survey %>%
  filter(type == "polynomial") %>%
  mutate(alpha = jitter(alpha)) %>%
  ggplot() +
  geom_point(aes(alpha, response_time/1000)) +
  geom_smooth(aes(alpha, response_time/1000)) +
  scale_y_log10() +
  xlab("Estimated alpha") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs estimated alpha for polynomial model")

For heteroskedasticity model, different \(a\) and distribution of \(x\) do not affect the response time much. Except for the uniform group, which has slightly lower response time.

Code

vi_survey %>%
  filter(type == "heteroskedasticity") %>%
  ggplot() +
  geom_boxplot(aes(factor(a), response_time/1000)) +
  facet_grid(~factor(x_dist, levels = c("uniform", "even_discrete", "normal", "lognormal"))) +
  scale_y_log10() +
  xlab("a") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs a for heteroskedasticity model faceted by distribution of x")

As \(b\) increases, when the subject correctly identify the actual data plot, the response time will decrease for \(n = 100\) and \(n = 300\).

Code

vi_survey %>%
  filter(type == "heteroskedasticity") %>%
  ggplot() +
  geom_boxplot(aes(factor(b), response_time/1000, group = b)) +
  scale_y_log10() +
  facet_grid(detect~n) +
  xlab("b") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs b for heteroskedasticity model faceted by number of observations and detection")

Similar to the polynomial model, \(\alpha\) does not affect the response time.

Code

vi_survey %>%
  filter(type == "heteroskedasticity") %>%
  mutate(alpha = jitter(alpha)) %>%
  ggplot() +
  geom_point(aes(alpha, response_time/1000)) +
  geom_smooth(aes(alpha, response_time/1000)) +
  scale_y_log10() +
  xlab("Estimated alpha") +
  ylab("Response time (seconds)") +
  ggtitle("Response time in log scale vs estimated alpha for heteroskedasticity model")

Selection

Code

vi_survey %>%
  rowwise() %>%
  mutate(p_value_rank = list((function(this_lineup) { 
    vi_lineup[[this_lineup]]$data %>%
      group_by(k) %>%
      summarise(p_value = first(p_value)) %>%
      arrange(k) %>%
      pull(p_value)
    })(unique_lineup_id))) %>%
  unnest_longer(p_value_rank) %>%
  View()

Code

vi_survey %>%
  filter(type == "heteroskedasticity") %>%
  ggplot() +
  geom_boxplot(aes(factor(a), response_time/1000)) +
  scale_y_log10()

Code

vi_survey %>%
  ggplot() +
  geom_point(aes(num_selection, log(response_time)))

Code

plot_lineup <- function(this_lineup) {
  VI_MODEL$plot_lineup(vi_lineup[[this_lineup]]$data, 
                       theme = theme_light(), 
                       remove_axis = TRUE, 
                       remove_legend = TRUE, 
                       remove_grid_line = TRUE,
                       add_zero_line = TRUE) -> p
  
  # x_mean <- mean(layer_scales(p)$x$range$range)
  # y_mean <- mean(layer_scales(p)$y$range$range)
  
  vi_survey %>%
    filter(unique_lineup_id == this_lineup) %>%
    # filter(unique_lineup_id == "heter_7") %>%
    mutate(selection = ifelse(num_selection == 0, paste(1:20, collapse = "_"), selection)) %>%
    mutate(num_selection = ifelse(num_selection == 0, 20, num_selection)) %>%
    (function(x) {
      for (i in 1:20)
        x[[paste0("plot_", i)]] <-
          grepl(paste0("_", i, "_"), x$selection) |
          grepl(paste0("^", i, "_"), x$selection) |
          grepl(paste0("_", i, "$"), x$selection) |
          grepl(paste0("^", i, "$"), x$selection)
      x}
    ) -> tmp_survey
  
  tmp_survey %>%
    group_by(unique_lineup_id) %>%
    summarise(across(plot_1:plot_20, sum)) %>%
    ungroup() %>%
    pivot_longer(plot_1:plot_20) %>%
    mutate(name = as.integer(gsub(".*_", "", name))) %>%
    rename(k = name, total_detect = value) -> plot_total_detect
  
  tmp_survey %>%
    mutate(across(plot_1:plot_20, function(x) x/num_selection)) %>%
    group_by(unique_lineup_id) %>%
    summarise(across(plot_1:plot_20, sum)) %>%
    ungroup() %>%
    pivot_longer(plot_1:plot_20) %>%
    mutate(name = as.integer(gsub(".*_", "", name))) %>%
    rename(k = name, weighted_selection = value) -> plot_weighted_selection
  
  k_col <- ifelse(plot_weighted_selection$weighted_selection == max(plot_weighted_selection$weighted_selection), 
                  "max_sel", 
                  ifelse(plot_weighted_selection$k == vi_lineup[[this_lineup]]$metadata$answer, 
                         "answer", 
                         "else")
                  )
  
  p +
    geom_polygon(data = merge(data.frame(k = 1:20,
                                         k_col = k_col),
                              data.frame(x = c(-Inf, Inf, Inf, -Inf),
                                         y = c(-Inf, -Inf, Inf, Inf))),
                 aes(x = x, y = y, col = k_col), fill = NA, size = 2) +
    geom_label(data = group_by(vi_lineup[[this_lineup]]$data, k) %>% 
                summarise(p_value = first(p_value)) %>%
                ungroup(),
              aes(x = -Inf, y = Inf, 
                  label = paste0(round(p_value, 2), " (", rank(p_value, ties.method = "min"), ")")), 
              hjust = 0, vjust = 1, alpha = 0.5, size = 2, label.padding = unit(0.1, "lines")) +
    scale_color_manual(values = c("max_sel" = "blue", "answer" = "red", "else" = "white")) +
    facet_wrap(~k, 
               labeller = as_labeller(
                 function(string, ...) 
                   paste(round(filter(plot_weighted_selection, k == as.integer(string))$weighted_selection, 3),
                         "/",
                         filter(plot_total_detect, k == as.integer(string))$total_detect)
                 )
               ) +
    ggtitle(paste0(this_lineup, 
                  "\neffect size: ", 
                  round(vi_lineup[[this_lineup]]$metadata$effect_size, 2),
                  ", visual p-value: ", 
                  round(filter(vi_survey, unique_lineup_id == this_lineup)$p_value[1], 2), 
                  ", conventional p-value: ",
                  round(filter(vi_survey, unique_lineup_id == this_lineup)$conventional_p_value[1], 2),
                  "\nshape: ",
                  filter(vi_survey, unique_lineup_id == this_lineup)$shape[1],
                  ", e_sigma: ",
                  filter(vi_survey, unique_lineup_id == this_lineup)$e_sigma[1],
                  ", x_dist: ",
                  filter(vi_survey, unique_lineup_id == this_lineup)$x_dist[1],
                  ", n: ",
                  filter(vi_survey, unique_lineup_id == this_lineup)$n[1]
                  ),
            subtitle =  paste0("Lineup evaluated by ",
                               vi_survey %>% filter(unique_lineup_id == this_lineup) %>% nrow(),
                               " subjects (c_i / total selections)"))

  # # Pop the last layers
  # tmp <- p$layers[[length(p$layers)]]
  # p$layers[[length(p$layers)]] <- NULL
  # 
  # # Append the element back to the list
  # p$layers <- append(list(tmp), p$layers)
  # 
  # p
} 

Code

VI_MODEL$plot_lineup(vi_lineup$poly_1$data, theme = theme_light(), remove_axis = TRUE, remove_legend = TRUE, remove_grid_line = TRUE)

Power

There are some cases where the proportion of detection does not match with the predicted power. When effect size < 10, there are points with power around 0.25, and when effect size > 10, there points with power lower than 0.25. Those will be the main focus.

Code

boot_y <- function(dat, y, times = 100) {
  map_df(1:times, function(i) slice_sample(dat, n = nrow(dat), replace = TRUE) %>% mutate(boot_id = i))
}

vi_survey %>%
  filter(x_dist == "uniform") %>%
  group_by(unique_lineup_id) %>%
  summarise(across(everything(), first)) %>%
  filter(type == "polynomial") %>%
  filter(e_sigma >= 0.5) %>%
  mutate(reject = as.numeric(p_value <= 0.05)) %>%
  ggplot() +
  geom_smooth(aes(effect_size, reject, col = "Visual"), method = "glm", method.args = list(family = binomial), se = FALSE) +
  geom_point(aes(effect_size, prop_detect, text = unique_lineup_id), alpha = 0.3) + 
  stat_smooth(geom = "line", 
              data = ~boot_y(.x, reject, times = 500), 
              aes(effect_size, reject, group = boot_id, col = "Visual"), 
              method = "glm", 
              method.args = list(family = binomial), 
              se = FALSE, 
              alpha = 0.03) +
  geom_smooth(data = mutate(poly_conv_sim, log_effect_size = log(effect_size)) %>%
                filter(x_dist == "uniform") %>%
                rename(F = F_p_value, BP = BP_p_value, SW = SW_p_value) %>%
                pivot_longer(F:SW) %>%
                mutate(reject = as.numeric(value < 0.05)), 
              aes(effect_size, reject, col = name), 
              method = "glm", 
              method.args = list(family = binomial),
              se = FALSE) +
  scale_x_log10() +
  xlab("Effect size") +
  ylab("Power") +
  ggtitle("Power of visual test with comparison to F, BP and SW test") -> p

plotly::ggplotly(p, tooltip = c("x", "y", "text"))

There are 10 lineups with effect size < 10 and power > 0.125, namely poly_108, poly_132, poly_159, poly_268, poly_295, poly_301, poly_357, poly_371, poly_39, poly_98.

Lineup poly_108 does not have a clear visual pattern. Subjects claimed to rely on outliers to identify the actual data plot.

Lineup poly_132 has a distracting plot positioning at panel 10, where subjects reported to see a shape from the plot. Those who correctly identifies the actual data plot suggest there are outliers in plot one, which is marginally true given there is a minor gap at the right of the plot.

Lineup poly_159 has a clear outlier at plot one, which is the reason three subjects detect it. There is another subject who detect it because of “shape”. This is kind of make sense as the outlier strengthen the shape.

Lineup poly_268 has an interesting plot at panel six, which happens to be the most selected plot. The actual data plot does not contain any visual detectable features, but two subjects still select this one. One select it with reason “cluster” and the other one select it with “shape”.

Lineup poly_295

Code

eval_info <- function(this_lineup) {
  vi_survey %>%
    filter(unique_lineup_id == this_lineup) %>%
    select(subject, selection, num_selection, detect, reason, confidence) %>%
    arrange(-detect, num_selection, reason, confidence) %>%
    gridExtra::tableGrob(rows = NULL, theme = gridExtra::ttheme_default(base_size = 8))
}

Code

for (this_lineup in vi_survey %>% 
  filter(x_dist == "uniform") %>% 
  group_by(unique_lineup_id) %>% 
  summarise(across(everything(), first)) %>% 
  filter(type == "polynomial") %>% 
  filter(effect_size < 10 & prop_detect > 0.125) %>% 
  .$unique_lineup_id %>%
  sort()) {
  gridExtra::grid.arrange(plot_lineup(this_lineup), eval_info(this_lineup), ncol = 2)
}

There are 7 lineups with effect size > 35 and power < 0.25, namely poly_199, poly_232, poly_251, poly_41, poly_422, poly_52, poly_529.

Code

for (this_lineup in vi_survey %>% 
     filter(x_dist == "uniform") %>% 
     group_by(unique_lineup_id) %>% 
     summarise(across(everything(), first)) %>% 
     filter(type == "polynomial") %>% 
     filter(effect_size > 35 & prop_detect < 0.25) %>% 
     .$unique_lineup_id %>%
  sort()) {
  gridExtra::grid.arrange(plot_lineup(this_lineup), eval_info(this_lineup), ncol = 2)
}

Replace effect size with expected effect size

Code

vi_survey %>%
  filter(x_dist == "uniform") %>%
  group_by(unique_lineup_id) %>%
  summarise(across(everything(), first)) %>%
  filter(type == "polynomial") %>%
  filter(e_sigma >= 0.5) %>%
  ggplot() +
  geom_point(aes(effect_size, expected_effect_size)) +
  geom_abline()

Code

boot_y <- function(dat, y, times = 100) {
  map_df(1:times, function(i) slice_sample(dat, n = nrow(dat), replace = TRUE) %>% mutate(boot_id = i))
}

vi_survey %>%
  filter(x_dist == "uniform") %>%
  group_by(unique_lineup_id) %>%
  summarise(across(everything(), first)) %>%
  filter(type == "polynomial") %>%
  filter(e_sigma >= 0.5) %>%
  mutate(reject = as.numeric(p_value <= 0.05)) %>%
  ggplot() +
  geom_smooth(aes(expected_effect_size, reject, col = "Visual"), method = "glm", method.args = list(family = binomial), se = FALSE) +
  geom_point(aes(expected_effect_size, prop_detect, text = unique_lineup_id), alpha = 0.3) + 
  stat_smooth(geom = "line", 
              data = ~boot_y(.x, reject, times = 500), 
              aes(expected_effect_size, reject, group = boot_id, col = "Visual"), 
              method = "glm", 
              method.args = list(family = binomial), 
              se = FALSE, 
              alpha = 0.03) +
  geom_smooth(data = mutate(poly_conv_sim, log_effect_size = log(effect_size)) %>%
                filter(x_dist == "uniform") %>%
                rename(F = F_p_value, BP = BP_p_value, SW = SW_p_value) %>%
                pivot_longer(F:SW) %>%
                mutate(reject = as.numeric(value < 0.05)), 
              aes(effect_size, reject, col = name), 
              method = "glm", 
              method.args = list(family = binomial),
              se = FALSE) +
  scale_x_log10() +
  xlab("Effect size") +
  ylab("Power") +
  ggtitle("Power of visual test with comparison to F, BP and SW test") -> p

plotly::ggplotly(p, tooltip = c("x", "y", "text"))